README.md

Pix2Pix

Tensorflow 2.0 Implementation of the paper Image-to-Image Translation using Conditional GANs by Philip Isola, Jun-Yan Zhu, Tinghui Zhou and Alexei A. Efros.

Architecture

Generator

• The Generator is a Unet-Like model with skip connections between encoder and decoder.
• Encoder Block is Convolution -> BatchNormalization -> Activation (LeakyReLU)
• Decode Blocks is Conv2DTranspose -> BatchNormalization -> Dropout (optional) -> Activation (ReLU)

Discriminator

• PatchGAN Discriminator
• Discriminator Block is Convolution -> BatchNormalization -> Activation (LeakyReLU)

Loss Functions

Generator Loss

The Loss function can also be boiled down to

Loss = GAN_Loss + Lambda * L1_Loss, where GAN_Loss is Sigmoid Cross Entropy Loss and Lambda = 100 (determined by the authors)

Discriminator Loss

The Discriminator Loss function can be written as

Loss = disc_loss(real_images, array of ones) + disc_loss(generated_images, array of zeros)

where disc_loss is Sigmoid Cross Entropy Loss.

Experiments with Standard Architecture

Experiment 1

Resource Credits: Trained on Nvidia Quadro M4000 provided by Paperspace Gradient.

Dataset: Facades

Result:

Experiment 2

Resource Credits: Trained on Nvidia Quadro P5000 provided by Paperspace Gradient.

Dataset: Maps

Result:

Experiment 3

Resource Credits: Trained on Nvidia Tesla V100 provided by DeepWrex Technologies.

Dataset: Cityscapes

Result:

Experiments with Mish Activation Function

Experiment 1 Mish

Resource Credits: Trained on Nvidia Quadro P5000 provided by Paperspace Gradient.

Dataset: Facades

Generator Architecture:

• The Generator is a Unet-Like model with skip connections between encoder and decoder.
• Encoder Block is Convolution -> BatchNormalization -> Activation (Mish)
• Decode Blocks is Conv2DTranspose -> BatchNormalization -> Dropout (optional) -> Activation (Mish)

Discriminator:

• PatchGAN Discriminator
• Discriminator Block is Convolution -> BatchNormalization -> Activation (Mish)

Result:

Experiment 2 Mish

Resource Credits: Trained on Nvidia Tesla P100 provided by Google Colab.

Dataset: Facades

Generator Architecture:

• The Generator is a Unet-Like model with skip connections between encoder and decoder.
• Encoder Block is Convolution -> BatchNormalization -> Activation (Mish)
• Decode Blocks is Conv2DTranspose -> BatchNormalization -> Dropout (optional) -> Activation (Mish)

Discriminator:

• PatchGAN Discriminator
• Discriminator Block is Convolution -> BatchNormalization -> Activation (ReLU)

Result:

Experiment 3 Mish

Resource Credits: Trained on Nvidia Quadro P5000 provided by Paperspace Gradient.

Dataset: Facades

Generator Architecture:

• The Generator is a Unet-Like model with skip connections between encoder and decoder.
• Encoder Block is Convolution -> BatchNormalization -> Activation (Mish)
• Decode Blocks is Conv2DTranspose -> BatchNormalization -> Dropout (optional) -> Activation (Mish) for the first three blocks are Conv2DTranspose -> BatchNormalization -> Dropout (optional) -> Activation (ReLU)

Discriminator:

• PatchGAN Discriminator
• Discriminator Block is Convolution -> BatchNormalization -> Activation (ReLU)

Result:

References

All the sources cited during building this codebase are mentioned below:

• Image-to-Image Translation with Conditional Adversarial Networks
• https://github.com/phillipi/pix2pix
• Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
• Mish: A Self Regularized Non-Monotonic Neural Activation Function
• Tensorflow Pix2Pix
• Keras Pix2Pix